Skip to main content
SpringerLink
Log in

Image Inpainting for Object Removal Application using Improved Patch Priority and Exemplar Patch Selection

  • Conference paper
  • First Online:
Renewable Energy, Green Computing, and Sustainable Development (REGS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2081))

  • 19 Accesses

Abstract

Image inpainting is a method that can be employed to repair damaged images and remove distracting elements. The effectiveness of image inpainting approach heavily relies on the computation of patch priority and the selection of exemplar patches in exemplar-based methods. The occurrence of the dropping effect in the computation of the most significant patch priority and the occurrence of matching errors in the selection of the best patch are the primary concerns in example inpaint approaches. The upgraded priority calculation approach is utilized to prevent the dropping effect and introduces a new similarity evaluating procedure called Square of Mean Difference (SMD). The effectiveness of the suggested strategies is evaluated by qualitatively evaluating them with the existing methods. The results demonstrate that the suggested methods surpassed the performance of the existing strategies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Rathish Kumar, B.V., Halim, A.: A linear fourth-order PDE-based gray-scale image inpainting model. Comput. Appl. Math. 38(6), 1–21 (2019)

    MathSciNet  Google Scholar 

  2. Sridevi, G., Srinivas Kumar, S.: Image inpainting and enhancement using fractional order variational model. Defense Sci. J. 67(3), 308–315 (2017)

    Article  Google Scholar 

  3. Sridevi, G., Srinivas Kumar, S.: P-Laplace variational image inpainting using symmetric Riesz fractional differential filter. Int. J. Electr. Comput. Eng. 7(2), 850–857 (2017)

    Google Scholar 

  4. Sridevi, G., Srinivas Kumar, S.: Image inpainting based on fractional-order nonlinear diffusion for image reconstruction. Circuits Syst. Signal Process. 38, 3802–3817 (2019)

    Article  Google Scholar 

  5. Sridevi, G., Kumar, S.: A qualitative report on diffusion based image inpainting models. Int. J. Comput. Digital Syst. 11(1), 369–386 (2022)

    Article  Google Scholar 

  6. Gamini, S., Gudla, V.V., Bindu, C.H.: Fractional-order diffusion based image denoising model. Int. J. Electr. Electron. Res. 10(4), 837–842 (2022)

    Article  Google Scholar 

  7. Gamini, S., Kumar, S.S.: Homomorphic filtering for the image enhancement based on fractional-order derivative and genetic algorithm. Comput. Electr. Eng. 106, 108566 (2023)

    Article  Google Scholar 

  8. Criminisi, A., Patrik, P., Kentaro, T.: Object removal by exemplar-based inpainting. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. II-II (2003)

    Google Scholar 

  9. Criminisi, A., Patrik, P., Kentaro, T.: Region filling and object removal by exemplar-based image inpainting. IEEE Trans. Image Process. 13(9), 1200–1212 (2004)

    Article  Google Scholar 

  10. Huang, C., Chun, H., Sheng, L., Ling, W.: Robust algorithm for exemplar-based image inpainting. In: Proceedings of International Conference on Computer Graphics, Imaging and Visualization, pp. 64–69, Beijing (2005)

    Google Scholar 

  11. Zongben, X., Jian, S.: Image inpainting by patch propagation using patch sparsity. IEEE Trans. Image Process. 19(5), 1153–1165 (2010)

    Article  MathSciNet  Google Scholar 

  12. Chinmayee, R., Anupama, A., Bagashree, P.: Image inpainting using exemplar based technique with improvised data term. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS), pp. 162–166, Belgaum (2018)

    Google Scholar 

  13. Liu, H., Bi, X., Lu, G., Wang, W.: Screen window propagating for image inpainting. IEEE Access 6, 61761–61772 (2019)

    Article  Google Scholar 

  14. Nan, A., Xi, X.: An improved Criminisi algorithm based on a new priority function and updating confidence. In: 2014 7th International Conference on Biomedical Engineering and Informatics, pp. 885–889. IEEE (2014)

    Google Scholar 

  15. Yao, F.: Damaged region filling by improved criminisi image inpainting algorithm for thangka. Clust. Comput. 22(6), 13683–13691 (2019)

    Article  Google Scholar 

  16. Janardhana Rao, B., Chakrapani, Y., Srinivas Kumar, S.: Image inpainting method with improved patch priority and patch selection. IETE J. Educ. 59(1), 26–34 (2018)

    Article  Google Scholar 

  17. Revathi, K., Janardhana Rao, B.: Analysis and implementation of enhanced image inpainting method using adjustable patch sizes. Int. J. 9(3) (2021)

    Google Scholar 

  18. Rao, B.J., Krishna, O.V.: Evaluation of image inpainting algorithms. CVR J. Sci. Technol. 7, 48–52 (2014)

    Article  Google Scholar 

  19. Zhang, L., Chang, M.: An image inpainting method for object removal based on difference degree constraint. Multimed. Tools Appl. 80, 4607–4626 (2021)

    Article  Google Scholar 

  20. Abdulla, A.A., Ahmed, M.W.: An improved image quality algorithm for exemplar-based image inpainting. Multimed. Tools Appl. 80(9), 13143–13156 (2021)

    Article  Google Scholar 

  21. Zahra, N., Ghazale, G., Nader, K., Shadrokh, S.: Image inpainting by adaptive fusion of variable spline interpolations. In: 25th International Computer Conference, Computer Society (CSICC), pp. 1–5, IEEE (2020)

    Google Scholar 

  22. Ahmed, M.W., Abdulla, A.A.: Quality improvement for exemplar-based image inpainting using a modified searching mechanism. UHD J. Sci. Technol. 4, 1–8 (2020)

    Article  Google Scholar 

  23. Janardhana Rao, B., Chakrapani, Y., Srinivas Kumar, S.: MABC-EPF: video in-painting technique with enhanced priority function and optimal patch search algorithm. Concurr. Comput. Pract. Exp. 34(11), e6840 (2022)

    Article  Google Scholar 

  24. Rao, B.J., Chakrapani, Y., Kumar, S.S.: An enhanced video inpainting technique with grey wolf optimization for object removal application. J. Mob. Multimed. 18(3), 561–582 (2022)

    Google Scholar 

  25. Janardhana Rao, B., Chakrapani, Y., Srinivas Kumar, S.: Video inpainting using advanced homography-based registration method. J. Math. Imaging Vis. 64(9), 1029–1039 (2022)

    Article  Google Scholar 

  26. Janardhana Rao, B., Chakrapani, Y., Srinivas Kumar, S.: Hybridized cuckoo search with multi-verse optimization-based patch matching and deep learning concept for enhancing video inpainting. Comput. J. 65(9), 2315–2338 (2022)

    Article  Google Scholar 

  27. Rao, B.J., Revathi, K., Babu, G.H.: Video inpainting using self-adaptive GMM with improved inpainting technique. CVR J. Sci. Technol. 22(1), 42–46 (2022)

    Google Scholar 

  28. Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. IEEE Trans. Pattern Anal. Mach. Intell.Intell. 33(5), 898–916 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. CVR College of Engineering, Hyderabad, India

    B. Janardhana Rao, Venkata Krishna Odugu & Harish Babu Gade

  2. Sphoorthy Engineering College, Hyderabad, India

    K. Revathi

  3. Vallurupalli Nageswara Rao Vignana Jyothi Institute of Engineering and Technology, Hyderabad, India

    Yalamanchili Bhanusree

Authors
  1. B. Janardhana Rao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Revathi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yalamanchili Bhanusree
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Venkata Krishna Odugu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Harish Babu Gade
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to B. Janardhana Rao .

Editor information

Editors and Affiliations

  1. CVR College of Engineering, Hyderabad, Telangana, India

    Sree Lakshmi Gundebommu

  2. Andhra University, Visakhapatnam, Andhra Pradesh, India

    Lakshminarayana Sadasivuni

  3. CVR College of Engineering, Hyderabad, India

    Lakshmi Swarupa Malladi

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rao, B.J., Revathi, K., Bhanusree, Y., Odugu, V.K., Gade, H.B. (2024). Image Inpainting for Object Removal Application using Improved Patch Priority and Exemplar Patch Selection. In: Gundebommu, S.L., Sadasivuni, L., Malladi, L.S. (eds) Renewable Energy, Green Computing, and Sustainable Development. REGS 2023. Communications in Computer and Information Science, vol 2081. Springer, Cham. https://doi.org/10.1007/978-3-031-58607-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-58607-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-58606-4

  • Online ISBN: 978-3-031-58607-1

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics

Search

Navigation